OpenAI-papers

OpenAI 真的是十分牛，因此整理了它在 Research 上的文章，如果有代码也一起呈上，好好学习，天天向上。

2024

• Measuring short-form factuality in large language models [pdf] [code]
• Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models [pdf]
• First-Person Fairness in Chatbots [pdf]
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering [pdf] [code]
• Rule Based Rewards for Language Model Safety [pdf] [code]
• Prover-Verifier Games improve legibility of LLM outputs [pdf]
• A Holistic Approach to Undesired Content Detection in the Real World [pdf]
• Improved Techniques for Training Consistency Models [pdf]
• Consistency Models [pdf]
• Scaling and evaluating sparse autoencoders [pdf] [code]
• The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions [pdf]

2023

• Let's Verify Step by Step [pdf]
• GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models [pdf]
• GPT-4 Technical Report [pdf]

2022

• Point-E: A System for Generating 3D Point Clouds from Complex Prompts [pdf] [code]
• Scaling Laws for Reward Model Overoptimization [pdf]
• Robust Speech Recognition via Large-Scale Weak Supervision [pdf] [code]
• Efficient Training of Language Models to Fill in the Middle [pdf] [code]
• Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos [pdf] [code]
• Evolution through Large Models [pdf]
• Teaching Models to Express Their Uncertainty in Words [pdf]
• Hierarchical Text-Conditional Image Generation with CLIP Latents [pdf]
• A Research Agenda for Assessing the Economic Impacts of Code Generation Models [pdf]
• Formal Mathematics Statement Curriculum Learning [pdf]
• Text and Code Embeddings by Contrastive Pre-Training [pdf]

2021

• WebGPT: Browser-assisted question-answering with human feedback [pdf]
• Training Verifiers to Solve Math Word Problems [pdf]
• TruthfulQA: Measuring How Models Mimic Human Falsehoods [pdf] [code]
• Evaluating Large Language Models Trained on Code [pdf]
• Multimodal Neurons in Artificial Neural Networks [pdf] [code]
• Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models [pdf]
• Learning Transferable Visual Models From Natural Language Supervision [pdf] [code]

2020

• Generative Language Modeling for Automated Theorem Proving [pdf]
• Generative Pretraining from Pixels [pdf] [code]
• Language Models are Few-Shot Learners [pdf]
• Measuring the Algorithmic Efficiency of Neural Networks [pdf]
• Jukebox: A Generative Model for Music [pdf] [code]
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [pdf]
• Scaling Laws for Neural Language Models [pdf]

2019

• Dota 2 with Large Scale Deep Reinforcement Learning [pdf]
• Deep Double Descent: Where Bigger Models and More Data Hurt [pdf]
• Leveraging Procedural Generation to Benchmark Reinforcement Learning [pdf] [code]
• Release Strategies and the Social Impacts of Language Models [pdf]
• Solving Rubik's Cube with a Robot Hand [pdf]
• Emergent Tool Use From Multi-Agent Autocurricula [pdf] [code]
• Release Strategies and the Social Impacts of Language Models [pdf] [code]
• Generating Long Sequences with Sparse Transformers [pdf] [code]
• Implicit Generation and Generalization in Energy-Based Models [pdf] [code]
• Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents [pdf] [code]
• Language Models are Unsupervised Multitask Learners [pdf] [code]
• Computational Limitations in Robust Classification and Win-Win Results [pdf]

2018

• An Empirical Model of Large-Batch Training [pdf]
• Quantifying Generalization in Reinforcement Learning [pdf] [code]
• Concept Learning with Energy-Based Models [pdf]
• Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control [pdf]
• Exploration by Random Network Distillation [pdf] [code]
• FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models [pdf]
• Large-Scale Study of Curiosity-Driven Learning [pdf] [code]
• Learning Dexterous In-Hand Manipulation [pdf]
• Variational Option Discovery Algorithms [pdf]
• Glow: Generative Flow with Invertible 1x1 Convolutions [pdf] [code]
• Learning Policy Representations in Multiagent Systems [pdf]
• GamePad: A Learning Environment for Theorem Proving [[pdf]][[code]](https://github.com/ml4tp/gamepad)
• Evolved Policy Gradients [pdf] [code]
• Gotta Learn Fast: A New Benchmark for Generalization in RL [pdf]
• Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines [pdf]
• Improving GANs Using Optimal Transport [pdf]
• On First-Order Meta-Learning Algorithms [pdf] [code]
• Some Considerations on Learning to Explore via Meta-Reinforcement Learning [pdf]
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research [pdf]
• DeepType: Multilingual Entity Linking by Neural Type System Evolution [pdf] [code]

2017

• GPU Kernels for Block-Sparse Weights [pdf] [code]
• Learning Sparse Neural Networks through \(L_0\) Regularization [pdf] [code]
• Interpretable and Pedagogical Examples [pdf]
• Meta Learning Shared Hierarchies [pdf] [code]
• Sim-to-Real Transfer of Robotic Control with Dynamics Randomization [pdf]
• Asymmetric Actor Critic for Image-Based Robot Learning [pdf]
• Domain Randomization and Generative Models for Robotic Grasping [pdf]
• Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments [pdf] [code]
• Emergent Complexity via Multi-Agent Competition [pdf] [code]
• Learning with Opponent-Learning Awareness [pdf] [code]
• Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation [pdf] [code]
• Parameter Space Noise for Exploration [pdf] [code]
• Proximal Policy Optimization Algorithms [pdf] [code]
• Synthesizing Robust Adversarial Examples [pdf]
• Hindsight Experience Replay [pdf]
• Teacher-Student Curriculum Learning [pdf] [code]
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments [pdf] [code]
• UCB Exploration via Q-Ensembles [pdf]
• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World [pdf]
• One-Shot Imitation Learning [pdf]
• Equivalence Between Policy Gradients and Soft Q-Learning [pdf]
• Stochastic Neural Networks for Hierarchical Reinforcement Learning [pdf] [code]
• Learning to Generate Reviews and Discovering Sentiment [pdf] [code]
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning [pdf]
• Emergence of Grounded Compositional Language in Multi-Agent Populations [pdf]
• Prediction and Control with Temporal Segment Models [pdf]
• Third-Person Imitation Learning [pdf] [code]
• PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications [pdf] [code]

2016

• Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning [pdf]
• On the Quantitative Analysis of Decoder-Based Generative Models [pdf] [code]
• A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models [pdf]
• \(\text{RL}^2\): Fast Reinforcement Learning via Slow Reinforcement Learning [pdf]
• Variational Lossy Autoencoder [pdf]
• Extensions and Limitations of the Neural GPU [pdf] [code]
• Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model [pdf]
• OpenAI Gym [pdf]
• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks [pdf]